Graduate eses and Dissertations Iowa State University Capstones, eses and Dissertations 2012 Factors affecting mycorrhizal colonization in Schizachyrium scoparium Paul N. Frater Iowa State University Follow this and additional works at: hps://lib.dr.iastate.edu/etd Part of the Ecology and Evolutionary Biology Commons is esis is brought to you for free and open access by the Iowa State University Capstones, eses and Dissertations at Iowa State University Digital Repository. It has been accepted for inclusion in Graduate eses and Dissertations by an authorized administrator of Iowa State University Digital Repository. For more information, please contact [email protected]. Recommended Citation Frater, Paul N., "Factors affecting mycorrhizal colonization in Schizachyrium scoparium" (2012). Graduate eses and Dissertations. 12818. hps://lib.dr.iastate.edu/etd/12818
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Graduate Theses and Dissertations Iowa State University Capstones, Theses andDissertations
2012
Factors affecting mycorrhizal colonization inSchizachyrium scopariumPaul N. FraterIowa State University
Follow this and additional works at: https://lib.dr.iastate.edu/etd
Part of the Ecology and Evolutionary Biology Commons
This Thesis is brought to you for free and open access by the Iowa State University Capstones, Theses and Dissertations at Iowa State University DigitalRepository. It has been accepted for inclusion in Graduate Theses and Dissertations by an authorized administrator of Iowa State University DigitalRepository. For more information, please contact [email protected].
Recommended CitationFrater, Paul N., "Factors affecting mycorrhizal colonization in Schizachyrium scoparium" (2012). Graduate Theses and Dissertations.12818.https://lib.dr.iastate.edu/etd/12818
Treseder, K., and P. Vitousek. 2001. Effects of soil nutrient availability on investment in
acquisition of N and P in Hawaiian rain forests. Ecology 82:946–954.
Wang, G., D. Stribley, P. Tinker, and C. Walker. 1993. Effects of pH on arbuscular
mycorrhiza I. Field observations on the long-‐term liming
experiments at Rothamsted and Woburn. New Phytologist 124:465–
472.
9
Chapter 2. Extent of mycorrhizal colonization in Schizachyrium scoparium along a latitude gradient
Abstract Resource stoichiometry has been proposed to influence rates of mycorrhizal
colonization in plants. This work has looked at environmental ratios of carbon,
nitrogen, and phosphorus (C:N:P); however, most of these studies have not looked at
overarching limitations or environmental controls on the plant-‐mycorrhizae
relationship (e.g. energetic influences such as temperature or light, water limitation, or
pH). I present research that assesses rates of mycorrhizal colonization in Schizachyrium
scoparium along a latitude gradient. I hypothesized that mycorrhizal colonization would
increase with decreasing latitude due to increased mean annual temperature (MAT)
and growing season length (GSL) at lower latitudes, which would allow plants to
photosynthesize both at higher rates and for longer periods throughout the year
thereby increasing carbon budget. Mycorrhizal colonization did increase with
decreasing latitude; however, using structural equation modeling (SEM) I show here
that aridity was the main driver for this pattern. MAT and GSL were not able to
significantly explain any patterns in the data. Similarly, differences in nutrients (i.e. N
and P) when accounting for other factors had no significant effect on the percentage of
mycorrhizal colonization in Schizachyrium scoparium.
10
Introduction The effect of nutrients and nutrient stoichiometry (specifically carbon -‐ C, nitrogen -‐ N,
and phosphorus -‐ P) on rates of mycorrhizal colonization has been studied extensively
for at least the past 20 years (Bååth and Spokes 1989, Johnson 1993, Rillig et al. 2000,
Titus and Leps 2000, Valentine et al. 2001, Treseder and Allen 2002, Johnson et al.
2003, Treseder 2004, Johnson 2009). Stoichiometry of resources has been hypothesized
to increase or decrease mycorrhizal colonization in plants due to changes in supply and
demand in biological markets of plants and mycorrhizal fungi (Schwartz and Hoeksema
1998, Johnson 2009). However, research has for the most part only looked at the
influences of soil nutrients and atmospheric carbon level on rates of mycorrhizal
colonization in plants making this research purely stoichiometric in nature (Johnson
2009). Carbon clearly plays an important role in the plant-‐mycorrhizae relationship
(Miller et al. 2002, Cameron et al. 2008), and much theoretical and recent empirical
work has been done to establish how and when plants should cooperate with
mycorrhizae and when they should abjure the relationship (Fitter 1991, 2006, Kiers et
al. 2011). One would expect, all else being equal, that if the acquisition of carbon were
to increase in plants, then the proportion of root system colonized by mycorrhizae
would increase as well due to increased carbon allocation from plants. This prediction
has been shown with experiments of increased atmospheric carbon (Rillig et al. 1999a,
1999b, 2000, Fransson et al. 2001, Lukac et al. 2003, Langley et al. 2003). However, no
one has looked at how increased carbon from energetic influences (i.e. temperature,
light) influences mycorrhizal colonization. Physiological principles of plant carbon
11
acquisition predict that photosynthetic rate increases with increased light and
temperature, and indeed, increases in temperature and light have been shown to
increase carbon acquisition in plants (Blackman and Matthaei 1905, Matthaei 1905).
Therefore, an increase in carbon acquisition via increased photosynthesis should lead
to higher rates of mycorrhizal colonization in plants. In addition to these factors
mycorrhizal colonization has been shown to be important in the acquisition of water
(Hardie and Leyton 1981, Mathur and Vyas 2000), and pH has been shown to have
variable results on the percentage of mycorrhizal colonization in plants with the
greatest rates of colonization seeming to occur at neutral pH values (Richards 1961,
Theodorou and Bowen 1969, Wang et al. 1993, Bollag and Leneowicz 1984). Therefore,
both water limitation and pH could also potentially play a large role in determining
rates of mycorrhizal colonization in plants. I was interested in investigating which
factors have the greatest effect on the percentage of mycorrhizal colonization in plants
across a range of levels for these seemingly important factors.
To assess how these various factors affect rates of mycorrhizal colonization I collected
root samples from Schizachyrium scoparium at five sites ranging in latitude from
Minnesota to Texas. These sites range in mean annual temperature (MAT) from 7.0 -‐
18.9° C and in growing season length (GSL) from 139 -‐ 235 days. This allows for a
natural gradient of temperature and light that would be difficult to attain with a
greenhouse study. This geographic gradient similarly contains a natural gradient of
nutrient conditions, which allowed me to test for the effects of nutrient level and
12
stoichiometry on rates of mycorrhizal colonization as well. Additionally, the sites that I
sampled are part of a replicated study of nutrient addition in grasslands, which extends
beyond the natural range of nutrient conditions. This presents a unique opportunity to
assess the status of this symbiosis with changes in nutrients as well as energetic
components thereby offering a method in which to test the combined effects of the
theory of ecological stoichiometry and energetic influences on rates of mycorrhizal
colonization. Nutrient levels differ at these sites with P increasing and N decreasing
with increasing latitude. While mycorrhizae are often considered more important when
soil P concentrations are lower it has been shown that nutrient levels at particular sites
determine how rates of mycorrhizal colonization respond to nutrient addition
(Treseder and Vitousek 2001). This suggests that nutrient levels at a particular site are
important in determining which nutrient (i.e. N or P) drive rates of mycorrhizal
colonization at that site. Similarly, N:P ratio could be an explanatory variable alternative
to strictly absolute values of N or P. A series of predictions can be made for how N, P,
MAT, GSL, aridity (as a metric for water limitation), and pH affect rates of mycorrhizal
colonization (Figs. 1-‐5). I hypothesized the following for each factor. Both higher N and
P concentrations would decrease mycorrhizal colonization; however, I also expected N
to be inversely correlated with latitude, but P to be positively correlated; I expected that
P concentration would have a greater effect on mycorrhizal colonization since the bulk
of literature has shown this (Treseder 2004), but that N may play a secondary role. N:P
ratio, which I hypothesized to decrease with increasing latitude, should have a positive
correlation with the percentage of mycorrhizal colonization. I predicted that both MAT
13
and GSL would increase the percentage of mycorrhizal colonization in plants, and
would be inversely correlated with latitude. Aridity, I hypothesized, would have a
positive correlation with both percentage of mycorrhizal colonization and an inverse
correlation with latitude. I predicted that the percentage of mycorrhizal colonization
would be affected by pH with a neutral pH having the greatest rates of mycorrhizal
colonization; however, I did not have a good prediction at whether pH would increase
or decrease along the latitude gradient sampled as I had not found any theoretical or
empirical work to back up any prediction for this. Due to differences in inter-‐site N and
P concentrations I expected both of these nutrients to have an effect on the percentage
of mycorrhizal colonization thereby negating any latitudinal effect that the individual
nutrients would have. Given the stoichiometric perspective of the relationship between
plants and mycorrhizal fungi, and the importance of C:N:P ratios, I hypothesized that
MAT and GSL would affect the percentage of mycorrhizal colonization the most. This
prediction stems from theory that plants will photosynthesize at higher rates with
increased MAT and GSL thereby increasing carbon for allocation to mycorrhizae, which
should increase percentage of colonization.
Methods Field Sites
I collected root samples of Schizachyrium scoparium at 5 replicated nutrient addition
sites that are part of The Nutrient Network. The Nutrient Network is a global research
cooperative that investigates top down and bottom up controls on grassland plant
communities by employing nutrient and herbivory treatments to grassland sites around
14
the world (Stokstad 2011). Replicate treatments of N, P, and K with micronutrients
were applied in a factorial block design to investigate singular and interactive effects of
nutrients on various aspects of plant communities. All fertilizers added were pellet
fertilizers that were added in the following quantities: N was added as time-‐release
urea (N2H4CO) at a rate of 23 kg hectare-‐1 year-‐1. P was added as triple super phosphate
(Ca(H2PO4)2) at a rate of 51 kg hectare-‐1 year-‐1. K and micronutrients were fertilized
together as a combination of potash (KCl) at a rate of 22 kg hectare-‐1 year-‐1 as well as
100 kg hectare-‐1 year-‐1 of Scotts Micromax, which is a blend of macro-‐ and micro-‐
nutrients (e.g. K, Ca, Mg, S, B, Cu, Fe, Mn, Mo, Zn). The 5 Nutrient Network sites where I
collected roots from were: Cedar Creek LTER, MN; Chichaqua Bottoms, IA (CBGB); Barta
Brothers Ranch, NE; Konza LTER, KS; and Temple, TX (which is located at the USDA-‐
ARS Grassland Soil and Water Research Laboratory, Temple, TX). I chose these sites
based on their involvement in the Nutrient Network and having replicate N, P, and K
additions. All treatments were applied to 5 x 5 meter plots set up in a randomized block
design. All sites had at least 3 replicate blocks with Cedar Creek LTER having 5 blocks
and CBGB having 6 blocks. Sites were also chosen based on the prevalence of
Schizachyrium scoparium at each site as well a location within the central regions of the
species’ range. I chose this species because it is a dominant grass across much of the
central United States, which makes findings widely applicable as well as allows me to
test questions about ecological stoichiometry across a wide geographical range. Sites
also contained additional herbivore exclosure treatments with one of these being un-‐
fertilized and another fully fertilized (i.e. with N, P, and K + micronutrients); however,
15
the exclusion of herbivores did not have a significant effect on the percentage of
mycorrhizal colonization and was not included in analyses.
Field and Lab Methods
I collected root samples from Schizachyrium scoparium plants using a 1.75 cm diameter
soil core to a depth of 30 cm. Samples were collected during the month of August, 2010.
I collected samples by taking the root core just off the center of the bunchgrass to avoid
destroying the meristem and harming the plant. Roots and soil samples were placed in
sealable plastic bags and stored in a cooler with ice while in transport to the lab. They
were then stored in a freezer at -‐20°C until processed. I retrieved roots from soil
samples by sieving the samples through a 2mm soil sieve. I obtained fresh mass of
roots, took a subsample between 0.05 g and 0.25 g for quantifying mycorrhizal
colonization, and dried the rest of the root sample at 60°C for 48 hours in order to
obtain a dry mass. The ratio between dry mass and fresh mass was used to calculate
dried mass of the subsample used for assessing mycorrhizal colonization. Roots were
stained in order to detect mycorrhizae using the trypan blue staining procedure
modified from (Robertson et al. 1999). Modifications consisted of tailoring clearing and
staining times to suit the roots of our study specimen as well as using filtered trypan
blue solution instead of trypan blue powder. I then assessed the percentage of root
sample colonized by mycorrhizal fungi using the grid-‐line intercept technique
(Giovannetti and Mosse 1980).
Soil Analysis
16
I collected the following soil variables for each soil sample collected from the field: total
C, total N, plant available P, pH, and texture. I was specifically interested in obtaining C,
N, and P in order to calculate stoichiometric ratios of these. Total N was obtained
instead of plant available N because of the time lag associated with transporting root
and soil samples from the field sites to the lab. I was concerned that denitrification of
inorganic N during transport would lead to an ill-‐represented sample of available N and
therefore decided to use total soil N instead of plant available N.
Total C and N were obtained through the Soil and Plant Analysis Laboratory at Iowa
State University using combustion analyses performed on a LECO CHN TruSpec and
Elementar Variomax using the methods found in Pella (1990). I obtained available P by
using the Mehlich III extract method and reading fluorescence at 630 nm on a BioTek
Synergy HT multi-‐mode microplate reader. N:P ratio was calculated by dividing ppm of
total N by ppm of available P. pH was measured by creating a slurry of 5 g of air dried
soil with 10 ml of distilled water and shaking for 5 minutes, and then reading pH using a
Fisher Scientific Accumet Basic AB15/15+ pH reader. Soil texture was taken from pre-‐
treatment data collected at each site by the Nutrient Network.
Environmental Variables
I used 30 year climate normals from the NOAA National Climatic Data Center to obtain
all climatic variables from the different sites (NOAA NCDC 2011). The climatic variables
that I used were mean annual temperature (MAT), growing season length (GSL), and
mean annual precipitation (MAP). I defined growing season length as the 90%
17
probability of the number of freeze free days above 0 degrees C (32˚ F). Soil information
was obtained from the USDA Web Soil Survey (Web Soil Survey 2011). Aridity Index
(AI) was used as a measure of water availability for plants. I calculated AI by the
formula AI = PET / P, where PET is potential evapotranspiration in mm and P is total
precipitation (in mm) (modified from UNEP 1997). Data on PET were obtained from
EOS-‐WEBSTER by the University of New Hampshire (EOS-‐WEBSTER 2012).
Statistical Analysis
All statistical analyses were performed in R statistical computing software (R
Development Core Team 2011). I used structural equation modeling (SEM) to parse out
effects of the various factors thought to have an influence on mycorrhizal colonization. I
also included latitude as having an effect on each of the factors in the model to control
for the correlation that latitude had on each factor. I used the “lavaan” package in R to
build models and assess model fit (Rosseel 2012) and used several model fit indices to
assess the fit of the model including the Tucker-‐Lewis Index (TLI), Comparative Fit
Index (CFI), chi-‐square, and root mean square error of approximation (RMSEA). I began
by building an initial model that included all of the factors hypothesized above (Fig. 7),
assessing fit of this full model using the above indices, and performing backwards
selection until best-‐fitting model was achieved.
18
Results Variables Across Latitude
The percentage of mycorrhizal colonization decreased significantly with increasing
latitude (p < 0.001, R2=0.14, Fig. 7). As expected, there were differences across latitude
among the explanatory data variables that I tested (Fig. 8-‐14). Total N, log N:P ratio, pH,
MAT, GSL, and aridity index (AI) all decreased with increasing latitude (N -‐ p < 0.0001;
R2=0.56, log N:P ratio -‐ p < 0.0001, R2=0.46; pH -‐ p < 0.0001, R2=0.68; MAT -‐ p < 0.0001,
R2=0.99; GSL -‐ p < 0.0001; R2=0.88; AI – p < 0.0001, R2=0.54), while available P
increased with increasing latitude (P -‐ p < 0.0001, R2=0.18).
Variables Affecting the Percentage of Mycorrhizal Colonization
The SEM with the best fitting model to the data included only pH, aridity index , and the
percentage of mycorrhizal colonization as exogenous variables (chi-‐square = 154, d.f. =
1, p =0.21; TLI = 0.993; CFI = 0.999; RMSEA = 0.05; Fig. 16). Within this best-‐fitting
model aridity index was the only variables that had a significant effect on the
percentage of mycorrhizal colonization across latitude (p = 0.05, standardized
coefficient=0.19). Since the original full model did not fit the data well this chosen
model is considered an exploratory analysis, which is why I only report standardized
coefficients for the model.
Discussion I found that the percentage of mycorrhizal colonization in Schizachyrium scoparium
19
decreases with increasing latitude. To my knowledge, there is only one other study that
looks at mycorrhizal fungi along a latitude gradient (Koske 1987), and this study looks
more at dominance, richness, and community composition of mycorrhizae over ~3.5° of
latitude. My study is novel in that it looks at rates of mycorrhizal colonization in plants
over a range of latitude of ~15°. Rates of other mutualisms have been studied with
regards to latitude, and it has been found that the number of mutualisms increase with
decreasing latitude or are higher in tropics than in temperate zones (Schemske et al.
2009). This is exactly in line with what I have found; however, mechanisms for this
pattern have not been established. I hypothesized that the increase in the percentage of
mycorrhizal colonization moving towards southerly latitudes would be related to
increased carbon acquisition from higher rates of photosynthesis due to higher MAT
and a longer time span to photosynthesize throughout the year (increased GSL).
However, aridity had a stronger influence on the percentage of mycorrhizal
colonization. This does not necessarily mean that temperature and light do not have an
influence on mycorrhizal colonization, but rather that aridity had a stronger influence
in this study. It is both theoretically justified (Gillooly et al. 2001, Brown et al. 2004)
and empirically shown that an increase in temperature will increase photosynthetic
rate and similarly carbon assimilation in plants (Lambers et al. 1998). According to
theory put forth by Johnson (2009) this should result in an increased percentage of
mycorrhizal colonization. Future work on this subject should include studies that
control for water and nutrient limitation, and assess rates of mycorrhizal colonization
along light and/or temperature gradients.
20
I interpret the effects of aridity on the percentage of mycorrhizal colonization in terms
of water limitation. Mycorrhizae have been shown to assist plants in the acquisition of
water (Hardie and Leyton 1981, Mathur and Vyas 2000). If plants are limited by water
(in terms of aridity), then it would make sense that this has an influence on the
percentage of mycorrhizal colonization found in their root systems. The effect of water
limitation on the percentage of mycorrhizal colonization could be tested by adding a
water treatment to arid sites and assessing the change in the percentage of mycorrhizal
colonization.
Aridity is important in terms of global change. As weather patterns continue to shift it
may be possible to see larger rain events with longer dry spells in between (IPCC 2012).
If this is the case plants may need to increase rates of mycorrhizal colonization in order
to avoid water limitation during those spells. This would represent a large carbon cost
for plants that previously would not have existed. Effects of this would lead to either
increased carbon costs on the plant or increased water limitation, both of which could
potentially have implications on populations of Schizachyrium scoparium and in turn
community dynamics in prairies as this plant is pervasive and dominant throughout
much of its range.
21
Fig. 1. Prediction of how the percentage of mycorrhizal colonization will change with increasing nitrogen or phosphorus levels in the soil.
1a
[N] or [P]
% C
olon
izat
ion
1b
N:P ratio
% C
olon
izat
ion
1c
MAT and GSL
% C
olon
izat
ion
1d
Aridity
% C
olon
izat
ion
1e
!"
#$%&'&()*+,)&(
pH = 7.0
1f
!"#$#%&'
(")#*+
N, pH, Aridity, MAT, GSL
P, pH
22
Fig. 2. Prediction of how the percentage of mycorrhizal colonization will change with increasing N:P ratio in the soil.
1a
[N] or [P]
% C
olon
izat
ion
1b
N:P ratio
% C
olon
izat
ion
1c
MAT and GSL
% C
olon
izat
ion
1d
Aridity
% C
olon
izat
ion
1e
!"
#$%&'&()*+,)&(
pH = 7.0
1f
!"#$#%&'
(")#*+
N, pH, Aridity, MAT, GSL
P, pH
23
Fig. 3. Prediction of how the percentage of mycorrhizal colonization will change with increasing mean annual temperature (MAT) and/or growing season length (GSL).
1a
[N] or [P]
% C
olon
izat
ion
1b
N:P ratio
% C
olon
izat
ion
1c
MAT and GSL
% C
olon
izat
ion
1d
Aridity
% C
olon
izat
ion
1e
!"
#$%&'&()*+,)&(
pH = 7.0
1f
!"#$#%&'
(")#*+
N, pH, Aridity, MAT, GSL
P, pH
24
Fig. 4. Prediction of how the percentage of mycorrhizal colonization will change with levels of soil pH.
1a
[N] or [P]
% C
olon
izat
ion
1b
N:P ratio
% C
olon
izat
ion
1c
MAT and GSL
% C
olon
izat
ion
1d
Aridity
% C
olon
izat
ion
1e
!"
#$%&'&()*+,)&(
pH = 7.0
1f
!"#$#%&'
(")#*+
N, pH, Aridity, MAT, GSL
P, pH
25
Fig. 5. Prediction of how the percentage of mycorrhizal colonization will change with increasing aridity.
Aridity
% C
olon
izat
ion
26
Fig. 6. Predictions of how each of these factors that potentially have an impact on the percentage of mycorrhizal colonization in plants will change across latitude. Note that pH is predicted both ways as there is not currently knowledge of how pH will change across this latitude gradient.
1a
[N] or [P]
% C
olon
izat
ion
1b
N:P ratio
% C
olon
izat
ion
1c
MAT and GSL
% C
olon
izat
ion
1d
Aridity
% C
olon
izat
ion
1e
!"
#$%&'&()*+,)&(
pH = 7.0
1f
!"#$#%&'
(")#*+
N, pH, Aridity, MAT, GSL
P, pH
27
Fig. 7. The relationship between the percentage of mycorrhizal colonization in Schizachyrium scoparium across latitude. This figure includes data points from all plots that were sampled including nutrient treatments. The pattern shown here is still significant when tested with control only plots as well (p < 0.05, R2=0.17)
32 34 36 38 40 42 44
2040
6080
Latitude
% C
olon
izat
ion
p < 0.001R2=0.14
28
Fig. 8. Regression of total N across latitude; total N decreases with increasing latitude (p < 0.0001, R2=0.56)
32 34 36 38 40 42 44
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
Latitude
tota
l N (%
)
p < 0.0001R2=0.56
29
Fig. 9. Regression of available P across latitude; available P increases with increasing latitude (p < 0.0001, R2=0.18)
32 34 36 38 40 42 44
02
46
810
Latitude
avai
labl
e P(µg/g)
p < 0.0001R2=0.18
30
Fig. 10. Regression of log N:P ratio across latitude; log N:P ratio decreases with increasing latitude (p < 0.0001, R2=0.46)
32 34 36 38 40 42 44
45
67
89
1011
Latitude
log
N:P
p < 0.0001R2=0.46
31
Fig. 11. Regression of mean annual temperature (MAT) across latitude; MAT decreases with increasing latitude (p < 0.0001, R2=0.99)
32 34 36 38 40 42 44
810
1214
1618
Latitude
MAT
p < 0.0001R2=0.99
32
Fig. 12. Regression of growing season length across latitude; growing season length decreases with increasing latitude (p < 0.0001, R2=0.88)
32 34 36 38 40 42 44
140
160
180
200
220
Latitude
Gro
win
g S
easo
n
p < 0.0001R2=0.88
33
Fig. 13. Regression of pH across latitude; pH decreases with increasing latitude (p < 0.0001, R2=0.68)
32 34 36 38 40 42 44
5.0
5.5
6.0
6.5
7.0
7.5
Latitude
pH
p < 0.0001R2=0.68
34
Fig. 14. Regression of aridity index (AI) across latitude; AI decreases with increasing latitude (p < 0.0001, R2=0.54)
32 34 36 38 40 42 44
0.050
0.055
0.060
0.065
0.070
0.075
0.080
Latitude
AI
p < 0.0001R2=0.54
35
Fig. 15 Full SEM model that I started with to explain which factors best explain the percentage of mycorrhizal colonization in Schizachyrium scoparium.
36
Fig. 16. SEM that has the best fit to the data (Chi-‐square=1.54, df=1, p=0.22; TLI=0.99; CFI=0.99; RMSEA=0.05). This model shows that only aridity had a significant effect on the percentage of mycorrhizal colonization in Schizachyrium scoparium across the latitude gradient sampled. Numbers reported are standardized coefficients.
37
References Bååth, E., and J. Spokes. 1989. The effect of added nitrogen and phosphorus on
mycorrhizal growth response and infection in Allium schoenoprasum.
Canadian Journal of Botany 67:3227–3232.
Blackman, F., and G. Matthaei. 1905. Experimental researches in vegetable assimilation
and respiration. IV. -‐-‐A quantitative study of carbon-‐dioxide assimilation
and leaf-‐temperature in natural illumination. Proceedings of the Royal
Society B: Biological Sciences 76:402–460. doi: 10.1098/rspb.1905.0037.
Bollag, J. M., and A. Leonowicz. 1984. Comparative studies of extracellular fungal
laccases. Applied and Environmental Microbiology 48:849–854.
Brown, J., J. Gillooly, A. Allen, V. Savage, and G. B. West. 2004. Toward a metabolic theory
of ecology. Ecology 85:1771–1789.
Cameron, D., I. Johnson, D. Read, and J. Leake. 2008. Giving and receiving: measuring the
carbon cost of mycorrhizas in the green orchid, Goodyera repens. New
Table 2. Soil texture for each site where samples were collected. Soiltexture information was obtained from Nutrient Network pre-treatmentdata.
Site % Sand % Silt % Clay
Cedar Creek LTER, MN 90 7 3Barta Brothers Ranch, NE 96 3.5 0.5Chichauqua Bottoms Greenbelt, IA 88 8 4Konza Prairie LTER, KS 19 56 25Temple, TX 31 31 38
59
3
Table
3.Total
carbon
,nitrogen,an
davailable
phosphorusforam
bient
(con
trol
plot)
soilsat
sites
wheremycorrhizal
sampleswerecollected.
Numbersreportedin
table
aremeans,
andnu
mbersin
parentheses
areSE.
Site
Tota
lC
(%)
Tota
lN
(%)
Available
P(µ
g/g)
C:N
N:P
Cedar
Creek
LTER,MN
0.6(0.06)
0.03
(0.004)
1.70
(0.13)
25.2
(4.0)
682(95)
Barta
BrothersRan
ch,NE
0.4(0.03)
0.03
(0.006)
0.13
(0.04)
16.3
(2.6)
17,066
(5731)
Chichau
quaBottomsGreenbelt,IA
0.5(0.08)
0.05
(0.008)
1.55
(0.13)
19.2
(6.6)
1373
(265)
Kon
zaPraireLTER,KS
4.0(0.2)
0.331(0.017)
0.36
(0.03)
13.2
(0.12)
34,772
(2295)
Tem
ple,TX
9.3(0.1)
0.268(0.017)
0.41
(0.05)
35.1(2.3)
27,690
(3567)
60
4
Table 4. Results from mixed-effects ANOVA treatment by site and all
interactions on the percentage of mycorrhizal colonization in Schizachyriumscoparium. Significant factors are reported in bold.
Factor d.f. F-value p-value
N 1 and 169 8.10 < 0.01P 1 and 169 1.82 0.18
K 1 and 169 0.41 0.53
Site 4 and 169 7.35 < 0.0001N x P 1 and 169 0.34 0.56
N x K 1 and 169 1.46 0.23
P x K 1 and 169 2.08 0.15
N x Site 4 and 169 0.87 0.48
P x Site 4 and 169 2.62 < 0.05K x Site 4 and 169 0.32 0.84
N x P x K 1 and 169 1.66 0.20
N x P x Site 4 and 169 0.69 0.60
N x K x Site 4 and 169 0.49 0.74
P x K x Site 4 and 169 0.45 0.77
N x P x K x Site 4 and 169 0.32 0.86
61
Fig. 17. Interaction plot showing the mean response to N addition across sites. Most sites decrease in the percentage of mycorrhizal colonization fairly consistently except for Konza, which shows an increase in the mean percentage of mycorrhizal colonization with N addition.
3035
4045
50
N Addition
Mea
n %
Col
oniz
atio
n
Site
templekonzabartacbgbcc
N Y
62
Fig. 18. Interaction plot showing the change in the mean percentage of mycorrhizal colonization with and without P addition. Sites show a wider range of responses to the addition of P, which is shown to be significant along ambient N:P ratios.
3035
4045
P Addition
Mea
n %
Col
oniz
atio
n
Site
templekonzacbgbbartacc
N Y
63
Fig. 19. The mean response of the percentage of mycorrhizal colonization to P addition per site plotted against mean ambient soil N:P ratios at respective sites. Response to P addition shows a significant U-‐shaped relationship along a range of N:P ratios (p < 0.05, R2=0.93). Error bars shown are standard errors of the mean response on the percentage of mycorrhizal colonization to P treatment and ambient N:P ratios at sites. Note that the points used in this correlation are mean values per site as opposed to actual responses.
0 5000 10000 15000 20000 25000 30000 35000
-20
-10
010
20
Ambient N:P
Mea
n R
espo
nse
in %
Col
oniz
atio
n P
p<0.05R2=0.93
64
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